| | DECEMBER 20219CIOReviewAs well as these repositories, several issuers have tried to answer this concern with the creation of their own investor portals todisplay information about their own transactions in one centralised hub.These dashboards contain a huge amount of data, but this does not mean the information is always displayed in an easy to understand format. At Kensington, we have tried to convert this information into a more user-friendly and graphical way, through the creation of a state-of-the-art data dashboard within our investor portal.Enabled by Microsoft Power BI to give the latest data visualisation capability, investors can view specific information about Kensington'ssecuritisations. This includes the production of stratification tables and visualisation of historical performance trends, for single or combined deals, in order to analyse mortgages that secure the Kensington RMBS programmes.The data dashboard manages over £6billion in mortgage assets. This type ofdata enriched platformprovides investors with an easy way to take a deep dive insight into the loans behind the investmentsthey are making. If investors wish to see how mortgage payment holidayborrowers are performing in the loan book by a particular region ­investors can selectby geographic region, see how many loans are there, and even narrow down to a particular loan-to-value (LTV) range. We allow investors to take a decisive view on the risk and if they are able to see what that risk is, with their own eyes, it allows them to make the best possible decision for their assets. As more securitisation issuers start to embed data visualisation tools like Power BI into their IT architecture they may also choose to roll out this capability.The three A's: Automation, analytics,AIData is one of the most important assets that currently exists. And businesses rely more and more on data models to help predict where they will be in three, five- and ten-years' time. Stochastic financial models that use historical data to forecast the performance of mortgages have been used in the US mortgage market for many years (for example the models developed by Kay Giesecke from Stanford). Over time the calibration of these models has been moved to machine learning frameworks. The US market has provided loan level performance data since before the financial crisis and so there is plenty of data to help calibrate these models. The most commonly used database is the one provided by Core Logic. However, access to such data for the UK mortgage market has not been so good. There are a few mortgage companies that have access to large amounts of data and have invested in the data infrastructure to determine that. Kensington is one of those, and we have developed a scenario model called Vector, and this is used in every aspect of the business. Developed over the last decade, the latest version uses machine learning (AI) to calibrate itself on our proprietary database and helps us predict how borrowers and the market may behave in a certain way in the future. If house prices go up (as the majority ofrecent HPIs are showing) or go down (as predicted after the stamp duty break finishes), we want to see how each mortgage portfolio will perform. We then aggregate this data together to see how the company will perform over time. Vector tracks exactly this, historic data, and predicts patterns. As with all thing's technology, the more data you input, the more you get out.The algorithms look at performance data and allows us to see what the greatest chances, or outcomes, are of a certain scenario. It can show us the primary factor that makes the difference to how that loan book performs. Whether that's LTV range, house prices, deposit amount, or other significant drivers of loan performances.If investors are looking to buy a book of loans, Vector can be used to assess what the book of loans is likely to generate over time and forecasts when repayments will take place and securitisations will be redeemed, or the percentage in arrears.On a macro level we can see how the company and portfolio will perform whether we are in a V, L, or U-shaped economic recovery and how many people may fall into arrears in each. The goldilocks formula Ultimately, the mortgage industry needs to fully embrace technology to remain competitive and there is no doubt that technology is one of the biggest opportunities to address industry challenges and drive future growth. Behind the scenes, analytics and technology play a vitalrole in manual underwritingand helpingus support borrowers who struggle to get help on the high street, while offering investors greater transparency and predictions in the hunt for yield. Data is one of the most important assets that currently exists. And businesses rely more and more on data models to help predict where they will be in three, five- and ten-years' time
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